Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations12623
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 MiB
Average record size in memory274.9 B

Variable types

Numeric9
Categorical3
Text1

Alerts

avg_transaction_amount is highly overall correlated with total_spentHigh correlation
total_spent is highly overall correlated with avg_transaction_amount and 1 other fieldsHigh correlation
transaction_count is highly overall correlated with total_spentHigh correlation
customer_service_interaction_count has 1093 (8.7%) zeros Zeros

Reproduction

Analysis started2025-08-01 19:45:32.393785
Analysis finished2025-08-01 19:45:39.781814
Duration7.39 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct53
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.869682
Minimum18
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size98.7 KiB
2025-08-01T20:45:39.867675image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q131
median44
Q357
95-th percentile68
Maximum70
Range52
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.312549
Coefficient of variation (CV)0.34904626
Kurtosis-1.1957031
Mean43.869682
Median Absolute Deviation (MAD)13
Skewness0.010559891
Sum553767
Variance234.47414
MonotonicityNot monotonic
2025-08-01T20:45:39.977880image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 263
 
2.1%
54 262
 
2.1%
32 259
 
2.1%
21 259
 
2.1%
41 256
 
2.0%
20 255
 
2.0%
40 255
 
2.0%
35 254
 
2.0%
38 254
 
2.0%
39 252
 
2.0%
Other values (43) 10054
79.6%
ValueCountFrequency (%)
18 233
1.8%
19 243
1.9%
20 255
2.0%
21 259
2.1%
22 237
1.9%
23 241
1.9%
24 263
2.1%
25 227
1.8%
26 225
1.8%
27 244
1.9%
ValueCountFrequency (%)
70 249
2.0%
69 239
1.9%
68 243
1.9%
67 226
1.8%
66 229
1.8%
65 216
1.7%
64 244
1.9%
63 247
2.0%
62 231
1.8%
61 233
1.8%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size768.6 KiB
female
8458 
male
4165 

Length

Max length6
Median length6
Mean length5.3400935
Min length4

Characters and Unicode

Total characters67408
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfemale
2nd rowmale
3rd rowfemale
4th rowmale
5th rowmale

Common Values

ValueCountFrequency (%)
female 8458
67.0%
male 4165
33.0%

Length

2025-08-01T20:45:40.080258image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-01T20:45:40.170920image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
female 8458
67.0%
male 4165
33.0%

Most occurring characters

ValueCountFrequency (%)
e 21081
31.3%
m 12623
18.7%
a 12623
18.7%
l 12623
18.7%
f 8458
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 67408
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 21081
31.3%
m 12623
18.7%
a 12623
18.7%
l 12623
18.7%
f 8458
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 67408
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 21081
31.3%
m 12623
18.7%
a 12623
18.7%
l 12623
18.7%
f 8458
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 67408
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 21081
31.3%
m 12623
18.7%
a 12623
18.7%
l 12623
18.7%
f 8458
12.5%
Distinct9219
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Memory size851.5 KiB
2025-08-01T20:45:40.425237image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length24
Median length21
Mean length12.064485
Min length5

Characters and Unicode

Total characters152290
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7288 ?
Unique (%)57.7%

Sample

1st rowcharlesville
2nd rownew scott
3rd rowjosephfort
4th rowlinland
5th rownorth lindamouth
ValueCountFrequency (%)
north 931
 
4.9%
west 926
 
4.9%
lake 913
 
4.8%
east 911
 
4.8%
new 886
 
4.7%
south 881
 
4.6%
port 880
 
4.6%
michael 84
 
0.4%
christopher 66
 
0.3%
david 59
 
0.3%
Other values (6463) 12414
65.5%
2025-08-01T20:45:40.783875image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 15948
 
10.5%
r 12822
 
8.4%
t 12629
 
8.3%
a 12553
 
8.2%
o 10503
 
6.9%
n 10121
 
6.6%
s 9069
 
6.0%
h 8988
 
5.9%
i 7636
 
5.0%
l 6976
 
4.6%
Other values (17) 45045
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 152290
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 15948
 
10.5%
r 12822
 
8.4%
t 12629
 
8.3%
a 12553
 
8.2%
o 10503
 
6.9%
n 10121
 
6.6%
s 9069
 
6.0%
h 8988
 
5.9%
i 7636
 
5.0%
l 6976
 
4.6%
Other values (17) 45045
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 152290
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 15948
 
10.5%
r 12822
 
8.4%
t 12629
 
8.3%
a 12553
 
8.2%
o 10503
 
6.9%
n 10121
 
6.6%
s 9069
 
6.0%
h 8988
 
5.9%
i 7636
 
5.0%
l 6976
 
4.6%
Other values (17) 45045
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 152290
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 15948
 
10.5%
r 12822
 
8.4%
t 12629
 
8.3%
a 12553
 
8.2%
o 10503
 
6.9%
n 10121
 
6.6%
s 9069
 
6.0%
h 8988
 
5.9%
i 7636
 
5.0%
l 6976
 
4.6%
Other values (17) 45045
29.6%

account_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size782.9 KiB
savings
6418 
loan
3134 
checking
3071 

Length

Max length8
Median length7
Mean length6.4984552
Min length4

Characters and Unicode

Total characters82030
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsavings
2nd rowsavings
3rd rowsavings
4th rowsavings
5th rowchecking

Common Values

ValueCountFrequency (%)
savings 6418
50.8%
loan 3134
24.8%
checking 3071
24.3%

Length

2025-08-01T20:45:40.891340image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-01T20:45:40.975618image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
savings 6418
50.8%
loan 3134
24.8%
checking 3071
24.3%

Most occurring characters

ValueCountFrequency (%)
s 12836
15.6%
n 12623
15.4%
a 9552
11.6%
i 9489
11.6%
g 9489
11.6%
v 6418
7.8%
c 6142
7.5%
l 3134
 
3.8%
o 3134
 
3.8%
h 3071
 
3.7%
Other values (2) 6142
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 82030
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 12836
15.6%
n 12623
15.4%
a 9552
11.6%
i 9489
11.6%
g 9489
11.6%
v 6418
7.8%
c 6142
7.5%
l 3134
 
3.8%
o 3134
 
3.8%
h 3071
 
3.7%
Other values (2) 6142
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 82030
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 12836
15.6%
n 12623
15.4%
a 9552
11.6%
i 9489
11.6%
g 9489
11.6%
v 6418
7.8%
c 6142
7.5%
l 3134
 
3.8%
o 3134
 
3.8%
h 3071
 
3.7%
Other values (2) 6142
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 82030
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 12836
15.6%
n 12623
15.4%
a 9552
11.6%
i 9489
11.6%
g 9489
11.6%
v 6418
7.8%
c 6142
7.5%
l 3134
 
3.8%
o 3134
 
3.8%
h 3071
 
3.7%
Other values (2) 6142
7.5%

login_frequency
Real number (ℝ)

Distinct30
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.329636
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size98.7 KiB
2025-08-01T20:45:41.061059image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q323
95-th percentile29
Maximum30
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.6517757
Coefficient of variation (CV)0.56438232
Kurtosis-1.1948195
Mean15.329636
Median Absolute Deviation (MAD)7
Skewness0.020286619
Sum193506
Variance74.853223
MonotonicityNot monotonic
2025-08-01T20:45:41.152645image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2 455
 
3.6%
3 449
 
3.6%
14 446
 
3.5%
21 445
 
3.5%
5 440
 
3.5%
23 437
 
3.5%
20 435
 
3.4%
19 435
 
3.4%
11 433
 
3.4%
7 431
 
3.4%
Other values (20) 8217
65.1%
ValueCountFrequency (%)
1 425
3.4%
2 455
3.6%
3 449
3.6%
4 420
3.3%
5 440
3.5%
6 405
3.2%
7 431
3.4%
8 420
3.3%
9 414
3.3%
10 416
3.3%
ValueCountFrequency (%)
30 429
3.4%
29 383
3.0%
28 427
3.4%
27 407
3.2%
26 361
2.9%
25 421
3.3%
24 403
3.2%
23 437
3.5%
22 388
3.1%
21 445
3.5%

transaction_count
Real number (ℝ)

High correlation 

Distinct491
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean253.86018
Minimum10
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size98.7 KiB
2025-08-01T20:45:41.345221image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile32
Q1129
median253
Q3379
95-th percentile475
Maximum500
Range490
Interquartile range (IQR)250

Descriptive statistics

Standard deviation143.18809
Coefficient of variation (CV)0.56404316
Kurtosis-1.2206212
Mean253.86018
Median Absolute Deviation (MAD)125
Skewness0.0072918614
Sum3204477
Variance20502.83
MonotonicityNot monotonic
2025-08-01T20:45:41.457184image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
453 45
 
0.4%
83 44
 
0.3%
236 41
 
0.3%
27 39
 
0.3%
19 39
 
0.3%
379 39
 
0.3%
420 38
 
0.3%
362 38
 
0.3%
448 38
 
0.3%
276 38
 
0.3%
Other values (481) 12224
96.8%
ValueCountFrequency (%)
10 25
0.2%
11 35
0.3%
12 25
0.2%
13 30
0.2%
14 30
0.2%
15 31
0.2%
16 20
0.2%
17 22
0.2%
18 19
0.2%
19 39
0.3%
ValueCountFrequency (%)
500 24
0.2%
499 18
0.1%
498 28
0.2%
497 27
0.2%
496 29
0.2%
495 27
0.2%
494 18
0.1%
493 21
0.2%
492 24
0.2%
491 21
0.2%

avg_transaction_amount
Real number (ℝ)

High correlation 

Distinct11872
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean505.8621
Minimum10
Maximum999.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size98.7 KiB
2025-08-01T20:45:41.561561image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile58.992
Q1258.23
median508.01
Q3755.415
95-th percentile949.304
Maximum999.89
Range989.89
Interquartile range (IQR)497.185

Descriptive statistics

Standard deviation285.85689
Coefficient of variation (CV)0.56508857
Kurtosis-1.2101704
Mean505.8621
Median Absolute Deviation (MAD)248.73
Skewness-0.0067265666
Sum6385497.3
Variance81714.161
MonotonicityNot monotonic
2025-08-01T20:45:41.665880image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
772.37 4
 
< 0.1%
464.02 4
 
< 0.1%
641.81 3
 
< 0.1%
377.06 3
 
< 0.1%
300.42 3
 
< 0.1%
236.38 3
 
< 0.1%
144.08 3
 
< 0.1%
471.23 3
 
< 0.1%
128.18 3
 
< 0.1%
144.09 3
 
< 0.1%
Other values (11862) 12591
99.7%
ValueCountFrequency (%)
10 1
< 0.1%
10.02 1
< 0.1%
10.04 1
< 0.1%
10.21 1
< 0.1%
10.53 1
< 0.1%
10.57 1
< 0.1%
10.6 1
< 0.1%
10.83 1
< 0.1%
10.87 1
< 0.1%
10.95 1
< 0.1%
ValueCountFrequency (%)
999.89 1
< 0.1%
999.83 1
< 0.1%
999.72 1
< 0.1%
999.66 1
< 0.1%
999.61 1
< 0.1%
999.6 1
< 0.1%
999.46 1
< 0.1%
999.42 1
< 0.1%
999.38 1
< 0.1%
999.35 1
< 0.1%

total_spent
Real number (ℝ)

High correlation 

Distinct12613
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127913.96
Minimum222
Maximum486452.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size98.7 KiB
2025-08-01T20:45:41.769573image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum222
5-th percentile7152.262
Q137207.71
median96697.44
Q3192873.3
95-th percentile352041.22
Maximum486452.96
Range486230.96
Interquartile range (IQR)155665.59

Descriptive statistics

Standard deviation109421.48
Coefficient of variation (CV)0.85543036
Kurtosis0.11216688
Mean127913.96
Median Absolute Deviation (MAD)68926.41
Skewness0.95732173
Sum1.6146579 × 109
Variance1.1973061 × 1010
MonotonicityNot monotonic
2025-08-01T20:45:41.871931image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
113002.24 2
 
< 0.1%
53915.68 2
 
< 0.1%
157353.84 2
 
< 0.1%
31989.24 2
 
< 0.1%
161848.75 2
 
< 0.1%
8223.75 2
 
< 0.1%
139320 2
 
< 0.1%
84738.18 2
 
< 0.1%
42349.44 2
 
< 0.1%
22878 2
 
< 0.1%
Other values (12603) 12603
99.8%
ValueCountFrequency (%)
222 1
< 0.1%
256.1 1
< 0.1%
328.8 1
< 0.1%
341.52 1
< 0.1%
350.22 1
< 0.1%
353.43 1
< 0.1%
425.04 1
< 0.1%
447.86 1
< 0.1%
471.52 1
< 0.1%
472.34 1
< 0.1%
ValueCountFrequency (%)
486452.96 1
< 0.1%
486266.2 1
< 0.1%
484515.36 1
< 0.1%
484160.37 1
< 0.1%
480372.48 1
< 0.1%
477548.28 1
< 0.1%
477303.05 1
< 0.1%
476954.64 1
< 0.1%
474702.52 1
< 0.1%
473938.68 1
< 0.1%

customer_service_interaction_count
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0529193
Minimum0
Maximum10
Zeros1093
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size98.7 KiB
2025-08-01T20:45:41.956626image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.1508909
Coefficient of variation (CV)0.62357832
Kurtosis-1.2107179
Mean5.0529193
Median Absolute Deviation (MAD)3
Skewness-0.018537315
Sum63783
Variance9.9281136
MonotonicityNot monotonic
2025-08-01T20:45:42.037264image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
4 1204
9.5%
9 1195
9.5%
6 1193
9.5%
7 1178
9.3%
10 1162
9.2%
1 1139
9.0%
3 1117
8.8%
2 1116
8.8%
5 1114
8.8%
8 1112
8.8%
ValueCountFrequency (%)
0 1093
8.7%
1 1139
9.0%
2 1116
8.8%
3 1117
8.8%
4 1204
9.5%
5 1114
8.8%
6 1193
9.5%
7 1178
9.3%
8 1112
8.8%
9 1195
9.5%
ValueCountFrequency (%)
10 1162
9.2%
9 1195
9.5%
8 1112
8.8%
7 1178
9.3%
6 1193
9.5%
5 1114
8.8%
4 1204
9.5%
3 1117
8.8%
2 1116
8.8%
1 1139
9.0%

churn
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size715.1 KiB
0
6330 
1
6293 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12623
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6330
50.1%
1 6293
49.9%

Length

2025-08-01T20:45:42.122208image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-01T20:45:42.195750image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 6330
50.1%
1 6293
49.9%

Most occurring characters

ValueCountFrequency (%)
0 6330
50.1%
1 6293
49.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12623
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6330
50.1%
1 6293
49.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12623
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6330
50.1%
1 6293
49.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12623
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6330
50.1%
1 6293
49.9%

registration_date_year
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2022.3101
Minimum2020
Maximum2025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size98.7 KiB
2025-08-01T20:45:42.261415image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum2020
5-th percentile2020
Q12021
median2022
Q32024
95-th percentile2025
Maximum2025
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6131685
Coefficient of variation (CV)0.00079768604
Kurtosis-1.1728377
Mean2022.3101
Median Absolute Deviation (MAD)1
Skewness0.082142262
Sum25527620
Variance2.6023126
MonotonicityNot monotonic
2025-08-01T20:45:42.339204image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2021 2323
18.4%
2022 2305
18.3%
2024 2271
18.0%
2023 2231
17.7%
2020 2203
17.5%
2025 1290
10.2%
ValueCountFrequency (%)
2020 2203
17.5%
2021 2323
18.4%
2022 2305
18.3%
2023 2231
17.7%
2024 2271
18.0%
2025 1290
10.2%
ValueCountFrequency (%)
2025 1290
10.2%
2024 2271
18.0%
2023 2231
17.7%
2022 2305
18.3%
2021 2323
18.4%
2020 2203
17.5%

registration_date_month
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2659431
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size98.7 KiB
2025-08-01T20:45:42.420887image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4311277
Coefficient of variation (CV)0.5475836
Kurtosis-1.1722386
Mean6.2659431
Median Absolute Deviation (MAD)3
Skewness0.10741564
Sum79095
Variance11.772637
MonotonicityNot monotonic
2025-08-01T20:45:42.510252image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
5 1194
9.5%
1 1150
9.1%
3 1137
9.0%
6 1133
9.0%
4 1097
8.7%
2 1077
8.5%
7 1075
8.5%
12 1004
8.0%
9 952
7.5%
8 942
7.5%
Other values (2) 1862
14.8%
ValueCountFrequency (%)
1 1150
9.1%
2 1077
8.5%
3 1137
9.0%
4 1097
8.7%
5 1194
9.5%
6 1133
9.0%
7 1075
8.5%
8 942
7.5%
9 952
7.5%
10 935
7.4%
ValueCountFrequency (%)
12 1004
8.0%
11 927
7.3%
10 935
7.4%
9 952
7.5%
8 942
7.5%
7 1075
8.5%
6 1133
9.0%
5 1194
9.5%
4 1097
8.7%
3 1137
9.0%

registration_date_day
Real number (ℝ)

Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.582746
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size98.7 KiB
2025-08-01T20:45:42.596026image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7769294
Coefficient of variation (CV)0.56324665
Kurtosis-1.1899269
Mean15.582746
Median Absolute Deviation (MAD)8
Skewness0.0095211826
Sum196701
Variance77.034489
MonotonicityNot monotonic
2025-08-01T20:45:42.693145image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 454
 
3.6%
2 447
 
3.5%
22 444
 
3.5%
5 441
 
3.5%
11 437
 
3.5%
16 436
 
3.5%
13 433
 
3.4%
25 433
 
3.4%
18 430
 
3.4%
14 429
 
3.4%
Other values (21) 8239
65.3%
ValueCountFrequency (%)
1 454
3.6%
2 447
3.5%
3 404
3.2%
4 399
3.2%
5 441
3.5%
6 387
3.1%
7 408
3.2%
8 422
3.3%
9 401
3.2%
10 420
3.3%
ValueCountFrequency (%)
31 214
1.7%
30 330
2.6%
29 389
3.1%
28 401
3.2%
27 425
3.4%
26 426
3.4%
25 433
3.4%
24 408
3.2%
23 382
3.0%
22 444
3.5%

Interactions

2025-08-01T20:45:38.776664image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:32.935540image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:33.778857image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:34.498929image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:35.260700image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:35.946810image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:36.620181image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:37.295816image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:38.071252image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:38.851491image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:33.058746image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:33.855583image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:34.577310image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:35.332333image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:36.019514image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:36.694936image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:37.366990image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:38.140769image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:38.930075image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:33.179986image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:33.933271image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:34.658022image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:35.416765image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:36.095719image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:36.775867image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:37.448035image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:38.218119image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:39.006620image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:33.276592image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:34.009928image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:34.806332image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:35.489899image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:36.170788image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:36.850177image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:37.521469image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:38.288052image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:39.083257image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:33.370034image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:34.084714image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:34.879159image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:35.569668image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:36.242704image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:36.928421image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:37.595998image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:38.363165image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:39.162993image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:33.454107image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:34.162439image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:34.955221image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:35.646854image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:36.313617image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:36.997153image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:37.672569image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:38.435605image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:39.242666image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:33.534041image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:34.252503image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:35.028825image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:35.722566image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:36.385791image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:37.071116image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:37.749262image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:38.512350image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:39.320020image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:33.617732image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:34.339095image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:35.108574image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:35.796117image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:36.463982image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:37.149140image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:37.828861image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:38.584227image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:39.392827image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:33.698538image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:34.416119image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:35.182307image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:35.870952image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:36.536500image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:37.220017image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:37.905492image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-08-01T20:45:38.702107image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Correlations

2025-08-01T20:45:42.767670image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
account_typeageavg_transaction_amountchurncustomer_service_interaction_countgenderlogin_frequencyregistration_date_dayregistration_date_monthregistration_date_yeartotal_spenttransaction_count
account_type1.0000.0000.0000.0190.0000.0000.0000.0090.0090.0000.0000.012
age0.0001.0000.0220.000-0.0120.022-0.0150.0130.0040.0030.0210.010
avg_transaction_amount0.0000.0221.0000.000-0.0000.0190.003-0.003-0.002-0.0100.668-0.012
churn0.0190.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.000
customer_service_interaction_count0.000-0.012-0.0000.0001.0000.0180.0070.025-0.0090.0100.0090.009
gender0.0000.0220.0190.0000.0181.0000.0000.0000.0000.0000.0130.003
login_frequency0.000-0.0150.0030.0000.0070.0001.000-0.012-0.001-0.0090.0040.005
registration_date_day0.0090.013-0.0030.0000.0250.000-0.0121.0000.0070.001-0.0050.003
registration_date_month0.0090.004-0.0020.000-0.0090.000-0.0010.0071.000-0.116-0.009-0.012
registration_date_year0.0000.003-0.0100.0000.0100.000-0.0090.001-0.1161.000-0.007-0.006
total_spent0.0000.0210.6680.0000.0090.0130.004-0.005-0.009-0.0071.0000.663
transaction_count0.0120.010-0.0120.0000.0090.0030.0050.003-0.012-0.0060.6631.000

Missing values

2025-08-01T20:45:39.508344image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-01T20:45:39.668838image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

agegenderlocationaccount_typelogin_frequencytransaction_countavg_transaction_amounttotal_spentcustomer_service_interaction_countchurnregistration_date_yearregistration_date_monthregistration_date_day
053femalecharlesvillesavings15182406.1873924.76002021813
140malenew scottsavings2669734.0250647.38402021722
268femalejosephfortsavings16273567.67154973.911020231127
331malelinlandsavings834212.317218.54102022623
420malenorth lindamouthchecking5468754.40353059.20802023123
536femaledarrellshirechecking2035283.099908.15512020216
629malethompsonchestersavings24474850.18402985.32802025414
729femalelake melodychecking1683774.4564279.35002024713
818femalemichaelshiresavings20366340.94124784.043120211019
931femalejonathanfortloan14111487.7354138.033020211110
agegenderlocationaccount_typelogin_frequencytransaction_countavg_transaction_amounttotal_spentcustomer_service_interaction_countchurnregistration_date_yearregistration_date_monthregistration_date_day
1261352malehigginstonsavings23112311.5834896.96112023531
1261434femalemichaelsideloan23357468.13167122.4181202254
1261553malewest emmasavings22412509.50209914.00102025226
1261654malemedinaviewsavings26180803.32144597.60202023317
1261762maleeast thomaschecking4232111.0325758.96202022115
1261852femaleelizabethburysavings9215290.6162481.154120231017
1261957femalepiercevilleloan13429536.11229991.19602021729
1262042femalebrucehavenchecking10257158.5840755.06812024927
1262121femalerobinsidesavings2123285.7935152.175020221011
1262229femaleeast davidloan1482763.5962614.38912023327